US11852716B2 - Apparatus and method for monitoring surrounding environment of vehicle - Google Patents
Apparatus and method for monitoring surrounding environment of vehicle Download PDFInfo
- Publication number
- US11852716B2 US11852716B2 US17/870,363 US202217870363A US11852716B2 US 11852716 B2 US11852716 B2 US 11852716B2 US 202217870363 A US202217870363 A US 202217870363A US 11852716 B2 US11852716 B2 US 11852716B2
- Authority
- US
- United States
- Prior art keywords
- grid
- parking
- grids
- vehicle
- stationary object
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 78
- 238000012544 monitoring process Methods 0.000 title claims abstract description 50
- 238000001514 detection method Methods 0.000 claims abstract description 72
- 238000013507 mapping Methods 0.000 claims abstract description 39
- 238000004422 calculation algorithm Methods 0.000 claims description 28
- 230000004044 response Effects 0.000 claims description 20
- 239000000284 extract Substances 0.000 claims description 7
- 230000008569 process Effects 0.000 description 32
- 238000010586 diagram Methods 0.000 description 21
- 230000006870 function Effects 0.000 description 18
- 230000015654 memory Effects 0.000 description 16
- 230000008859 change Effects 0.000 description 11
- 230000006399 behavior Effects 0.000 description 10
- 238000012545 processing Methods 0.000 description 9
- 238000000605 extraction Methods 0.000 description 7
- 238000012937 correction Methods 0.000 description 5
- 238000006073 displacement reaction Methods 0.000 description 4
- VHYFNPMBLIVWCW-UHFFFAOYSA-N 4-Dimethylaminopyridine Chemical compound CN(C)C1=CC=NC=C1 VHYFNPMBLIVWCW-UHFFFAOYSA-N 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 241001025261 Neoraja caerulea Species 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000012212 insulator Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005442 molecular electronic Methods 0.000 description 1
- 239000002071 nanotube Substances 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 229920000642 polymer Polymers 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/04—Monitoring the functioning of the control system
- B60W50/045—Monitoring control system parameters
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/06—Systems determining position data of a target
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/06—Systems determining position data of a target
- G01S13/42—Simultaneous measurement of distance and other co-ordinates
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/52—Discriminating between fixed and moving objects or between objects moving at different speeds
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/408—Radar; Laser, e.g. lidar
-
- B60W2420/52—
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/20—Static objects
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Y—INDEXING SCHEME RELATING TO ASPECTS CROSS-CUTTING VEHICLE TECHNOLOGY
- B60Y2400/00—Special features of vehicle units
- B60Y2400/30—Sensors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
- G01S2013/9314—Parking operations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
- G01S2013/9315—Monitoring blind spots
Definitions
- Exemplary embodiments of the present disclosure relate to an apparatus and method for monitoring the surrounding environment of a vehicle, and more particularly, to an apparatus and method for monitoring the surrounding environment of a vehicle by using an OGM (Occupancy Grid Map).
- OGM Olecupancy Grid Map
- a radar for a vehicle refers to a device that detects an outside object within a detection area when the vehicle travels, and warns a driver to help the driver to safely drive the vehicle.
- FIGS. 1 A and 1 B illustrate areas to which general radars for a vehicle transmit radar signals to detect an outside object.
- the radar for a vehicle operates to transmit a radar signal according to a frame with a predefined period, and detect an outside object.
- the signal characteristics of the transmitted radar signal such as a waveform, frequency, distance resolution, angle resolution, maximum sensing distance, and FoV (Field of View) are different depending on a system of the vehicle, to which the radar is applied. Examples of the system include a DAS (Driver Assistance System) such as BSD (Blind Spot Detection), LCA (Lane Change Assistance), or RCTA (Rear Cross Traffic Alert).
- an apparatus for monitoring a surrounding environment of a vehicle including a sensor unit including a plurality of detection sensors for detecting an object outside a vehicle according to a frame at a time period, and a controller configured to extract a stationary object from among the outside objects detected by the sensor unit, map the extracted stationary object to a grid map, calculate an occupancy probability parameter, indicative of a probability of the stationary object being located on a grid of the grid map, from a result of mapping, and monitor the surrounding environment of the vehicle by specifying the grid on which the stationary object is located in the grid map, based on the occupancy probability parameter, apply a clustering algorithm to the specified grid to create a cluster composed of a plurality of grids having the same characteristic, determine a type of parking in a parking space by extracting an edge grid of the cluster to detect a plurality of other vehicles continuously parked around the vehicle, and control a function of a rear cross traffic alert (ROTA) system, in response to the type of parking.
- ROTA rear cross traffic alert
- the controller may be configured to determine a peak grid with a maximum occupancy probability parameter from among grids in the grid map, and to determine that the stationary object is located on the peak grid, in response to the occupancy probability parameter of the peak grid being equal to or greater than a threshold value for the peak grid, and the peak grid on which the stationary object is determined to be located may include a plurality of peak grids.
- the controller may be configured to use a density based spatial clustering of applications with noise (DBSCAN) algorithm as the clustering algorithm to create one or more clusters, and the clustering criterion of the DBSCAN algorithm corresponds to a distance between the plurality of peak grids where the stationary object is determined to be located.
- DBSCAN density based spatial clustering of applications with noise
- the controller may be configured to extract grids in which a peak grid does not exist in an immediately adjacent grid, as edge grids of each cluster, from among the grids constituting the cluster, and determine nearest edge grids, which are at positions closest to the vehicle, from among the extracted edge grids, and to determine the nearest edge grids as grids on which the plurality of other vehicles are continuously parked around the vehicle are located, in response to the nearest edge grids being continuously arranged.
- the controller may be configured to determine the type of parking in the parking space, based on a first ratio between longitudinal and transverse lengths of the plurality of other vehicles corresponding to the continuously arranged nearest edge grids and a second ratio between a standard deviation of longitudinal positions and a standard deviation of transverse positions of the plurality of other vehicles.
- the controller may be configured to determine the type of parking in the parking space by determining a parking angle corresponding to the first and second ratios using a parking angle function in relation to a combination of the first and second ratios and comparing the parking angle with a threshold value, and the type of parking may include one of orthogonal parking, diagonal parking, and parallel parking.
- the controller may be configured to correct a form of a tracking gate of the ROTA for tracking a target vehicle moving in the parking space based on the type of parking in the parking space.
- the controller may be configured to correct the tracking gate in the form of an ellipse with a transverse major axis, an ellipse with a diagonal major axis, and an ellipse with a longitudinal major axis, in response to the type of parking in the parking space being one of the orthogonal parking, the diagonal parking, and the parallel parking, respectively.
- a processor-implemented method of monitoring a surrounding environment of a vehicle including extracting, by a controller, a stationary object from among objects, outside a vehicle, detected by a sensor unit including a plurality of detection sensors for detecting the outside objects of the vehicle according to a frame at a time period, mapping, by the controller, the extracted stationary object to a preset grid map, calculating, by the controller, an occupancy probability parameter indicative of a probability that the stationary object being located on a grid of the grid map from a result of mapping, monitoring, by the controller, the surrounding environment of the vehicle by specifying the grid on which the stationary object is located in the grid map, based on the occupancy probability parameter, creating, by the controller, a cluster composed of a plurality of grids having the same characteristic by applying a predefined clustering algorithm to the specified grid, determining, by the controller, a type of parking in a parking space by extracting an edge grid of the cluster to detect a plurality of other vehicles continuously parked around the vehicle, and controlling
- the monitoring of the surrounding environment of the vehicle may include determining a peak grid with a maximum occupancy probability parameter from among grids in the grid map, and determining that the stationary object is located on the peak grid, in response to the occupancy probability parameter of the peak grid being equal to or greater than a threshold value defined for the peak grid, and the peak grid on which the stationary object is determined to be located may include a plurality of peak grids.
- the creating of the cluster may include using a density based spatial clustering of applications with noise (DBSCAN) algorithm as the clustering algorithm to create one or more clusters, and the clustering criterion of the DBSCAN algorithm corresponds to a distance between the plurality of peak grids where the stationary object is determined to be located.
- DBSCAN density based spatial clustering of applications with noise
- the determining of the type of parking in the parking space may include extracting grids in which a peak grid does not exist in an immediately adjacent grid, as edge grids of each cluster, from among the grids constituting the cluster, and determining nearest edge grids, which are at positions closest to the vehicle, from among the extracted edge grids, and determining those nearest edge grids as grids on which the plurality of other vehicles are continuously parked around the vehicle are located, in response to the determined nearest edge grids being continuously arranged.
- the determining of the type of parking in the parking space may include determining the type of parking in the parking space, based on a first ratio between longitudinal and transverse lengths of the plurality of other vehicles corresponding to the continuously arranged nearest edge grids and a second ratio between a standard deviation of longitudinal positions and a standard deviation of transverse positions of the plurality of other vehicles.
- the determining of the type of parking in the parking space may include determining the type of parking in the parking space by determining a parking angle corresponding to the first and second ratios using a parking angle function in relation to a combination of the first and second ratios and comparing the parking angle with a threshold value, and the type of parking may include one of orthogonal parking, diagonal parking, and parallel parking.
- the controlling of the function of the rear cross traffic alert (ROTA) system may include correcting a form of a tracking gate of the ROTA for tracking a target vehicle moving in the parking space based on the type of parking in the parking space.
- ROTA rear cross traffic alert
- the controlling of the function of the rear cross traffic alert (ROTA) system may include correcting the tracking gate in the form of an ellipse with a transverse major axis, an ellipse with a diagonal major axis, and an ellipse with a longitudinal major axis, in response to the type of parking in the parking space being one of the orthogonal parking, the diagonal parking, and the parallel parking, respectively.
- ROTA rear cross traffic alert
- FIGS. 1 A and 1 B are diagrams illustrating areas to which general radars for a vehicle transmit radar signals to detect an outside object.
- FIG. 2 is a block configuration diagram for describing an apparatus for monitoring the surrounding environment of a vehicle in accordance with an embodiment of the present disclosure.
- FIG. 3 is a diagram illustrating a grid map in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure.
- FIGS. 4 to 8 are diagrams illustrating a process of setting threshold values of the grid map in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure.
- FIGS. 9 A to 10 are diagrams illustrating a process of updating the grid map in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure.
- FIG. 11 is a diagram illustrating a process of mapping a stationary object to the grid map in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure.
- FIGS. 12 to 14 are diagrams illustrating a process of deciding an expanded mapping area in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure.
- FIGS. 15 to 16 B are diagrams illustrating a process of correcting an occupancy probability parameter in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure.
- FIGS. 17 to 20 are diagrams illustrating a process of correcting a shaded grid in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure.
- FIGS. 21 to 26 are diagrams illustrating a process of monitoring a continuous structure in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure.
- FIGS. 27 to 32 are diagrams illustrating a process of performing ROTA function control in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure.
- FIG. 33 is a flowchart for explaining a method of monitoring a surrounding environment of a vehicle in accordance with an embodiment of the present disclosure.
- first,” “second,” and “third,” A, B, C, (a), (b), (c), or the like may be used herein to describe various members, components, regions, layers, or sections, these members, components, regions, layers, or sections are not to be limited by these terms. Rather, these terms are only used to distinguish one member, component, region, layer, or section from another member, component, region, layer, or section.
- a first member, component, region, layer, or section referred to in the examples described herein may also be referred to as a second member, component, region, layer, or section without departing from the teachings of the examples.
- FIG. 2 is a block configuration diagram for describing an apparatus for monitoring the surrounding environment of a vehicle in accordance with an embodiment of the present disclosure
- FIG. 3 is a diagram illustrating a grid map in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure
- FIGS. 4 to 8 are diagrams illustrating a process of setting threshold values of the grid map in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure
- FIGS. 9 A to 10 are diagrams illustrating a process of updating the grid map in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure
- FIGS. 11 to 11 are a diagram illustrating a process of mapping a stationary object to the grid map in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure
- FIGS. 12 to 14 are diagrams illustrating a process of deciding an expanded mapping area in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure
- FIGS. 15 , 16 A, and 16 B are diagrams illustrating a process of correcting an occupancy probability parameter in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure
- FIGS. 17 to 20 are diagrams illustrating a process of correcting a shaded grid in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure.
- FIGS. 21 to 26 are diagrams illustrating a process of monitoring a continuous structure in the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure.
- the apparatus for monitoring the surrounding environment of a vehicle in accordance with the embodiment of the present disclosure may include a sensor unit 100 and a control unit 200 (may also be referred to as the controller 200 ).
- the sensor unit 100 may include first to fourth detection sensors 110 , 120 , 130 , and 140 corresponding to radar sensors of the vehicle.
- the first detection sensor 110 may correspond to a rear right (RR) radar sensor
- the second detection sensor 120 may correspond to a rear left (RL) radar sensor
- the third detection sensor 130 may correspond to a front right (FR) radar sensor
- the fourth detection sensor 140 may correspond to a front left (FL) radar sensor. Therefore, the detection sensors 110 , 120 , 130 , and 140 may operate to detect an outside object through a method of transmitting a radar signal according to frames with a predefined period and receiving a signal reflected from the outside object.
- DAS Driver Assistance System
- the waveform, frequency, distance resolution, angle resolution, maximum sensing distance, and FoV of a radar signal transmitted from the radar sensor may have different characteristics for the respective frames.
- the control unit 200 serves to monitor the surrounding environment of the vehicle by controlling an operation of the DAS of the vehicle, and may be implemented as an ECU (Electronic Control Unit), processor, CPU (Central Processing Unit) or SoC (System on Chip).
- the control unit 200 may drive an operating system or application to control a plurality of hardware components or software components connected to the control unit 200 , and perform various data processing operations.
- control unit 200 may operate to extract a stationary object among outside objects detected by the sensor unit 100 by using behavior information of the vehicle, map the extracted stationary object to a preset grid map, and add occupancy information to each of grids constituting the grid map depending on whether the stationary object is mapped to the grid map. Furthermore, the control unit 200 may operate to calculate an occupancy probability parameter indicating the probability that the stationary object will be located at each of the grids, from the occupancy information added to the grids within the grid map in a plurality of frames to be monitored, and monitor the surrounding environment of the vehicle on the basis of the calculated occupancy probability parameter.
- control unit 200 may extract a stationary object among outside objects detected by the sensor unit 100 by using behavior information of the vehicle and object information acquired on the basis of a result obtained by detecting the outside objects through the sensor unit 100 . That is, the descriptions of the present embodiment will be focused on the configuration for monitoring a stationary object, not a moving object, among various outside objects around the vehicle.
- the behavior information of the vehicle may include a vehicle speed, yaw rate, speed change information, and steering angle
- the object information may include the number of outside objects detected by the sensor unit 100 , the longitudinal distance and horizontal distance to each of the objects, the longitudinal speed and horizontal speed of each of the objects, and the intensity of a received signal.
- the control unit 200 may extract only a stationary object among the outside objects by using the behavior information of the vehicle and the object information. For example, the control unit 200 may distinguish between a moving object and a stationary object by analyzing the relationships between the vehicle speed of the vehicle and the longitudinal/horizontal speeds of the objects, in order to extract only the stationary object.
- control unit 200 may map the extracted stationary object to the preset grid map. Before the mapping process for the stationary object, the grid map and an update process for the grid map will be preferentially described.
- the grid map may be set in the control unit 200 in advance, and have a size corresponding to the surrounding environment area of the vehicle, which is to be monitored.
- X map_max represents the maximum distance in the longitudinal direction (the longitudinal size of the grid map)
- Y map_max represents the maximum distance in the horizontal direction (the horizontal size of the grid map)
- X map_min represents a longitudinal reference position of the grid map
- Y map_min represents a horizontal reference position of the grid map
- X map_step represents the longitudinal size of each grid
- Y map_step represents the horizontal size of each grid.
- the longitudinal and horizontal axes of the grid map may be set on the basis of the vehicle. If the longitudinal and horizontal axes of the grid map are set on the basis of a specific point, not the vehicle, more memory resources may be required depending on the mileage of the vehicle. Furthermore, it is effective to set, to the surrounding area of the vehicle, a surrounding environment monitoring area required for outputting a warning to a driver or performing a traveling control operation of the vehicle. Therefore, the longitudinal and horizontal axes of the grid map may be set on the basis of the vehicle.
- the indexes (coordinates (i, j)) of the grids constituting the grid map may also be set on the basis of the vehicle, where i and j represent the longitudinal and horizontal indexes, respectively.
- a threshold value for deciding whether a stationary object occupies each of the grids within the grid map may be defined for the corresponding grid in the grid map.
- the threshold value functions as a value which is compared to an occupancy probability parameter, and serves as a reference value for determining whether the stationary object is located at the corresponding grid.
- the threshold value may be defined for each of the grids on the basis of a mathematical model according to the intensity of a received signal inputted to the sensor unit 100 , and the mathematical model may correspond to a well-known radar equation below, where Pr represents the intensity of the received signal, Gt represents an antenna gain, and Rt represents the distance to the object:
- the intensity of the received signal may differ depending on the antenna gain and the relative distance to the object. Therefore, the probability that the same object will be detected through the radar may differ depending on the location thereof. For example, when an object is located at a short distance, the intensity of a received signal is so high that the object detection probability increases, and when an object is located at a long distance, the intensity of a received signal is so low that the object detection probability decreases.
- the waveform, frequency, distance resolution, angle resolution, maximum sensing distance, and FoV of a radar signal transmitted from the radar may have different characteristics for the respective frames, depending on the DAS (e.g. BSD, LCA or ROTA) of the vehicle, to which the radar sensor is applied.
- each of the frames may include an area where an object can be repeatedly detected, and only a specific frame may include an area where an object can be detected.
- the area which is repeated in each of the frames may have a high object detection probability, and the area which is not repeated in each of the frames may have a low object detection probability. That is because, during two frames, an object can be detected twice in an area which is repeated, but an object can be detected only once in an area which is not repeated.
- the RR radar sensor and the RL radar sensor there may be an area where an object can be redundantly detected through the two radar sensors, and an area where an object can be detected only through one radar sensor. Therefore, the area where the object can be redundantly detected through the two radar sensors may have a high object detection probability, and the area where the object can be detected only through one radar sensor may have a low object detection probability. That is because, although one radar sensor does not detect the object in the area where the object can be redundantly detected through the two radar sensors, the object can be detected through the other adjacent radar sensor, but when one radar sensor does not detect the object in the area where the object can be detected only through one radar sensor, the object cannot be detected through the other adjacent radar sensor.
- the threshold values for the respective grids may be differently set depending on the object detection probability, which makes it possible to prevent the false determination (false detection and missing detection).
- the threshold value may be set to different values for an independent area, a single-overlap area, and a multi-overlap area within the grid map.
- the independent area may be defined as an area within the grid map, which is sensed by the first detection sensor 110 in a K th frame, where K is a natural number
- the single-overlap area may be defined as an area within the grid map, in which an independent area and an area sensed by the first detection sensor 110 overlap each other in a (K+1) th frame distinguished from the K th frame (following the K th frame). That is, the independent area and the single-overlap area are distinguished from each other, according to whether the detection areas overlap each other for the same detection sensor in the respective frames.
- the grid of the independent area is designated by ‘0’
- the grid of the single-overlap area is designated by ‘1’.
- the threshold value of the grid of the independent area may be set to a lower value than that of the grid of the single-overlap area, which makes it possible to compensate for false detection and missing detection which may occur for an object located in the independent area.
- the multi-overlap area may be defined as an area within the grid map, in which an area sensed by the second detection sensor 120 adjacent to the first detection sensor 110 overlaps a single-overlap area in the same frame (K th or (K+1) th frame). That is, the multi-overlap area is decided according to whether areas detected by two adjacent detection sensors overlap each other in the same frame.
- the grid of an area sensed by the first detection sensor 110 is designated by ‘0’
- the grid of the area where areas sensed by the first and second detection sensors 110 and 120 overlap each other is designated by ‘1’.
- the grid map may be divided into the independent area ‘0’ sensed by the first detection sensor 110 in the K th frame, the single-overlap area ‘1’ which is an overlap area between the areas sensed by the first detection sensor 110 in the K th frame and the (K+1) th frame, and the multi-overlap area ‘2’ which is an overlap area sensed by the first and second detection sensors 110 and 120 in the same frame and overlaps the single-overlap area.
- the threshold values of the independent area, the single-overlap area, and the multi-overlap area are defined as a first threshold value, a second threshold value, and a third threshold value, respectively, a relationship of ‘first threshold value ⁇ second threshold value ⁇ third threshold value’ may be established in a section where the threshold values linearly increase as illustrated in FIG. 8 .
- the indexes of the grid map are changed by the behavior of the vehicle.
- a process of updating the grid map by changing the indexes of the grid map is needed in order to map a stationary object to the grid map.
- the index of the grid to which the stationary object is mapped needs to be changed according to the behavior of the vehicle.
- the grid map is updated after the stationary object is mapped to the grid map, the index of the grid to which the stationary object is mapped is also changed.
- the control unit 200 may update the grid map when a longitudinal moving distance of the vehicle is larger than the longitudinal size of the grid or a horizontal moving distance of the vehicle is larger than the horizontal size of the grid during a period from a (K ⁇ 1) th frame to the K th frame.
- the control unit 200 may change the indexes of the respective grids in the (K ⁇ 1) th frame from those in the K th frame, on the basis of the longitudinal moving distance, the horizontal moving distance, and a longitudinal angle change of the vehicle.
- FIG. 9 A illustrates the grid map in the (K ⁇ 1) th frame with the index of the grid at which the stationary object is located.
- the index of the stationary index on the grid map in the (K ⁇ 1) th frame needs to be changed on the basis of the K th frame, because the index of the stationary object on the grid map in the K th frame is different from the index of the stationary object on the grid map in the (K ⁇ 1) th frame.
- the index of the stationary object on the grid map in the (K ⁇ 1) th frame needs to be changed on the basis of the K th frame, because the index of the stationary object on the grid map in the K th frame is different from the index of the stationary object on the grid map in the (K ⁇ 1) th frame.
- an angle change based on the yaw rate may be reflected into the update of the grid map.
- the control unit 200 calculates the accumulative values of yaw-axis angle changes and moving displacement changes of the vehicle during a period from the (K ⁇ 1) th frame to the K th frame, according to Equation 1 below.
- sin( ⁇ ) â y ⁇ _acc ⁇ _acc+ ⁇ [Equation 1]
- Equation 1 ⁇ represents a yaw-axis reference instantaneous angle change of the vehicle, ⁇ _acc represents a yaw-axis reference accumulative angle change during the period from the (K ⁇ 1) th frame to the K th frame, ⁇ represents an instantaneous moving displacement of the vehicle, Vs represents the speed of the vehicle, dt represents a time period from the (K ⁇ 1) th frame to the K th frame, represents a longitudinal unit vector, represents a horizontal unit vector, and ⁇ _acc represents an accumulative moving displacement of the vehicle during the period from the (K ⁇ 1) th frame to the K th frame.
- the control unit 200 determines whether a grid map update condition is satisfied, according to Equation 2 below.
- ⁇ x k ⁇ cos( ⁇ )
- Equation 2 ⁇ x k represents a longitudinal instantaneous moving distance of the vehicle, ⁇ y k represents a horizontal instantaneous moving distance of the vehicle, ⁇ x k _acc represents a longitudinal accumulative moving distance of the vehicle, and ⁇ y k _acc represents a horizontal accumulative moving distance of the vehicle.
- the control unit 200 updates the grid map according to Equation 3 below.
- Equation 3 (i, i) represents the index of a grid, (i_update, j_update) represents the index of an updated grid, and floor represents a truncation operator.
- the matrix functions as a rotation matrix for rotating the grid map according to the yaw rate of the vehicle:
- the control unit 200 may convert the location information of a stationary object, i.e. the longitudinal distance and horizontal distance to the stationary object, into an index corresponding to the (updated) grid map, according to Equation 4 below.
- I tgt_n represents the longitudinal index of a target grid
- J tgt_n represents the horizontal index of the target grid
- X tgt_n represents the longitudinal distance to the stationary object
- Y tgt_n represents the horizontal distance to the stationary object.
- the control unit 200 may map an extracted stationary object to the grid map by specifying a target grid of the grid map, corresponding to a changed index.
- the control unit 200 may add occupancy information having a first value to the target grid to which the stationary object is mapped, and add occupancy information having a second value to the other grids.
- the first value may be set to ‘1’
- the second value may be set to ‘0’.
- the value ‘1’ may be added as the occupancy information to the target grid to which the stationary object is mapped
- the value ‘0’ may be added as the occupancy information to the other grids to which the stationary object is not mapped.
- the occupancy information added to an index (i, j) in the K th frame will be represented by Pmap (i, j, k).
- the waveform, frequency, distance resolution, angle resolution, maximum sensing distance, and FoV of a radar signal transmitted from a radar sensor may have different characteristics for the respective frames, depending on the DAS (e.g. BSD, LCA or ROTA) of the vehicle, to which the radar sensor is applied. Therefore, although the same stationary object is detected, the index at which the stationary object is detected may be changed in each frame because the signal characteristics are different in each frame. In this case, an occupancy probability parameter to be described below may be reduced by the number of used signal waveforms.
- FIG. 12 illustrates results obtained when the radar sensor detects the same stationary object by transmitting radar signals with a single waveform and multiple waveforms.
- grids occupied in the respective frames are distributed to reduce the probability that the stationary object will be detected, compared to the single waveform.
- the threshold value of the grid map is set to a low value to compensate for the reduction in the occupancy probability parameter, the stationary object is highly likely to be falsely detected due to a clutter or noise.
- the control unit 200 may add occupancy information to surrounding grids as well as the target grid corresponding to the detected stationary object. Specifically, as illustrated in FIG. 13 , the control unit 200 may decide an expanded mapping area, which is expanded by a preset range on the basis of the target grid to which the stationary object is mapped, and calculate the occupancy probability parameter by adding the occupancy information with the first value to each of the grids constituting the expanded mapping area, in order to monitor the surrounding environment of the vehicle.
- the preset range expanded from the target grid may be defined in advance by a designer, in consideration of the similarity (distance resolution and speed resolution) between the signal waveforms.
- FIG. 14 illustrates results obtained when the radar sensor detects the same stationary object by transmitting radar signals with a single waveform and multiple waveforms.
- the process of calculating the occupancy probability parameter of the grid map in the present embodiment follows an occupancy probability calculation method of a general OGM (Occupancy Grid Map) based on Equation 5 below.
- Equation 5 k represents the sensing data (the above-described object information) of the sensor unit 100 (radar sensor) from the first frame to the K th frame, and V 1:k represents the behavior data (the above-described behavior information) of the vehicle from the first frame to the K th frame, and I0 represents a prior probability (0 in the present embodiment).
- an occupancy probability parameter p is calculated according to Equation 6 below.
- Equation 6 M represents the number of frames to be monitored.
- the speed, moving displacement, and yaw-axis angle change of the vehicle which serve as factors for determining whether the update condition of the grid map is satisfied, are acquired by the sensors applied to the vehicle. Since such sensing values inevitably contain an error, it may be determined that the update condition of the grid map has been satisfied even though the update condition of the grid map was not actually satisfied, due to the error contained in the sensing values. In this case, the grid map may be falsely updated. As described above, during the update process for the grid map, the control unit 200 operates to change the index of the target grid to which the stationary object is mapped. Thus, when the grid map is falsely updated, an error may occur between the index corresponding to the actual location of the stationary object and the index of the stationary object mapped to the falsely updated grid map. As a result, the error may cause false detection and missing detection for the stationary object.
- FIG. 15 A illustrates that a stationary object is mapped to a grid ⁇ circumflex over ( 1 ) ⁇ in the (K ⁇ 1) th frame, and then the grid ⁇ circle around ( 1 ) ⁇ is expanded by a preset range to decide a first expanded mapping area
- FIG. 15 B illustrates that the update condition of the above-described grid map is satisfied in the K th frame, such that the grid map is updated. Since the grid map has been updated, the index of the grid to which the stationary object is mapped is also changed, so that the grid to which the stationary object is mapped is updated into a grid 0.
- the location of the stationary object which has been actually detected by the sensor unit 100 , is still maintained at the grid ⁇ circle around ( 1 ) ⁇ .
- an error occurs between the index of the grid corresponding to the actual location of the stationary object and the index of the grid of the stationary object mapped to the updated grid map.
- the control unit 200 may correct the respective occupancy probability parameters of the grids constituting a second expanded mapping area through a method of comparing the first expanded mapping area in the (K ⁇ 1) th frame to the second expanded mapping area in the K th frame, thereby correcting the above-described update error.
- the control unit 200 may specify a first area composed of grids whose occupancy probability parameters have increased, among the grids of the second expanded mapping area, on the basis of the K th frame over the (K ⁇ 1) th frame. That is, the first area corresponds to grids which were not occupied in the (K ⁇ 1) th frame, but are occupied in the K th frame. Furthermore, the control unit 200 may specify a second area composed of grids whose occupancy probability parameters have decreased, among the grids of the first expanded mapping area, on the basis of the K th frame over the (K ⁇ 1) th frame. That is, the second area corresponds to grids which were occupied in the (K ⁇ 1) th frame, but are not occupied in the K th frame.
- control unit 200 may correct the respective occupancy probability parameters of the grids constituting the second expanded mapping area in the K th frame by substituting the occupancy probability parameters of the second area with the occupancy probability parameters of the first area.
- the expanded mapping area may be configured while being matched with the location of the stationary object, which is actually detected by the sensor unit 100 .
- the occupancy probability parameters of the grids may be reset to ‘0’.
- FIG. 16 A illustrates an example of an occupancy probability parameter on the grid map before an update error of the grid map is updated.
- a grid ⁇ circle around ( 1 ) ⁇ corresponds to a location having horizontal/longitudinal errors from the actual location of a stationary object, but remains with a predetermined occupancy probability value
- a grid ⁇ circle around ( 2 ) ⁇ corresponds to the actual location of the stationary object, but has a lower occupancy probability value than surrounding grids, because the grid ⁇ circle around ( 2 ) ⁇ is a newly occupied grid.
- FIG. 16 B illustrates an example of the occupancy probability parameter on the grid map after an update error of the grid map is corrected.
- a grid ⁇ circle around ( 1 ) ⁇ is a previously occupied grid, and has a low occupancy probability value through resetting
- a grid ⁇ circle around ( 2 ) ⁇ corresponds to the actual location of a stationary object, and has a higher occupancy probability value than surrounding grids because the grid ⁇ circle around ( 2 ) ⁇ is a newly occupied grid, but inherits a predetermined occupancy probability value.
- the detection sensor in accordance with the present embodiment may be implemented as a radar sensor. As illustrated in FIG. 17 , a shaded area where the radar sensor cannot detect an outside object occurs due to the FoV and mounting characteristics (mounting angle and position) of the radar sensor.
- control unit 200 may operate to correct the shaded grid by using a first method of receiving an occupancy probability parameter in the (K ⁇ 1) th frame or a second method of receiving an occupancy probability parameter of a grid around the shaded grid.
- the first method may be performed when the speed of the vehicle is equal to or higher than a preset reference value.
- a grid ⁇ circle around ( 1 ) ⁇ in the (K ⁇ 1) th frame does not correspond to a shaded grid, and thus retains with an occupancy probability parameter.
- the update process for the grid map is performed, and the grid ⁇ circle around ( 1 ) ⁇ in the K th frame belongs to the shaded grids.
- control unit 200 may set the occupancy probability parameter of the grid ⁇ circle around ( 1 ) ⁇ in the (K ⁇ 1) th frame to the occupancy probability parameter of the shaded grid ⁇ circle around ( 1 ) ⁇ in the K th frame, thereby minimizing a loss caused by missing detection of the radar sensor.
- the second method may be performed when the speed of the vehicle is lower than the reference value. That is, when the vehicle travels at a very low speed or is stopped, the grid map is not updated even though the (K ⁇ 1) th frame is switched to the K th frame. Thus, the first method cannot be applied.
- the control unit 200 may operate to set the occupancy probability parameter of a grid around a shaded grid to the occupancy probability parameter of the shaded grid. In this case, as illustrated in FIG. 19 , the control unit 200 may perform the second method from a shaded grid located at the outermost position, in order to acquire the occupancy probability parameter of a grid which is not the shaded grid.
- the control unit 200 may set the highest occupancy probability parameter, among the occupancy probability parameters of grids located within a preset range (e.g. one grid) from the shaded grid, to the occupancy probability parameter of the corresponding shaded grid.
- FIGS. 20 A and 20 B show a result obtained by setting an occupancy probability parameter with a predetermined value to a shaded grid through the correction for the shaded area.
- control unit 200 may operate to specify the grid at which the stationary object is highly likely to be located, on the basis of the occupancy probability parameters of the grids within the expanded mapping area.
- the control unit 200 may decide a peak grid having the highest occupancy probability parameter among the grids within the expanded mapping area decided for a plurality of frames to be monitored.
- the control unit 200 may determine that the stationary object is located at the peak grid.
- the control unit 200 may monitor the surrounding environment of the vehicle by repeatedly performing the stationary object location decision method based on the ‘peak detection’, while the vehicle travels.
- the peak grid on which the stationary object is determined to be located may consist of a plurality of peak grids in relation to the driving of the vehicle.
- the plurality of peak grids determined as described above may be clustered according to whether they have the same characteristic (i.e., whether they correspond to the same physical structure).
- those grids may be determined as grids on which continuous structures around the vehicle are located.
- the determined continuous structures may correspond to other vehicles, guardrails, or the like parked continuously in the parking lot. Accordingly, the control unit 200 may cause the vehicle to park or travel by avoiding the continuous structure therearound.
- This continuous structure monitoring process may be divided into a cluster creation process and an edge grid extraction process.
- a predefined clustering algorithm may be applied to the peak grid specified in the process of “7. Stationary Object Location Decision (Peak Detection)” to create one or more clusters composed of a plurality of grids having the same characteristic.
- the clustering algorithm may correspond to a density based spatial clustering of applications with noise (DBSCAN) algorithm.
- DBSCAN density based spatial clustering of applications with noise
- the DBSCAN algorithm is an algorithm that clusters a high-density part where points are concentrated, which is known as a strong model for noise and outlier identification.
- the distance between the peak grids where stationary objects are located is applied as the clustering criterion of the DBSCAN algorithm. That is, the cluster is composed of a set of peak grids where the distance between the peak grids is less than or equal to the reference value or they are immediately adjacent to each other.
- FIG. 21 illustrates an example of the created cluster.
- control unit 200 may operate to extract the edge grid of each cluster and to monitor continuous structures around the vehicle. Specifically, the control unit 200 may monitor the continuous structures around the vehicle by determining the edge grid of each cluster and a nearest edge grid extracted therefrom.
- the control unit 200 may extract that grid as the edge grid of the cluster.
- FIG. 22 illustrates an example of extracting the edge grid of the cluster.
- FIG. 22 ( a ) illustrates grids constituting the cluster, wherein it is determined whether a peak grid present in a grid immediately adjacent to that grid using Equation 7 below.
- the edge flag in a “test grid”, since all the immediately adjacent grids correspond to peak grids, the edge flag is set to a value of 0 in Equation 7, and in an “adjacent grid”, since at least one of the immediately adjacent grids does not correspond to a peak grid, the edge flag is set to a value of 1 in Equation 7.
- an edge grid in which the edge flag is set to a value of 1 may be extracted as illustrated in FIG. 22 ( c ) .
- the edge flag extraction process in FIG. 22 and Equation 7 is performed to secure memory resources and reduce the amount of computation and computational complexity in the process of determining a nearest edge flag to be described later.
- the control unit 200 may determine a nearest edge grid, which is at a position closest to the vehicle, from among the extracted edge grids.
- the longitudinal axis, transverse axis, and index of the grid map are set with respect to the vehicle. Accordingly, the grid map may be divided into first to fourth quadrants with respect to the vehicle as illustrated in FIG. 21 .
- the positions of the first to fourth quadrants are the same as the positions of the quadrants in the normal X-Y coordinate system.
- control unit 200 may operate to determine the nearest edge grid of that cluster in a variable manner depending on a quadrant in which that cluster exists among the first to fourth quadrants.
- the control unit 200 may, i) when a cluster exists in the first quadrant, determine, as a nearest edge grid, a grid with minimum longitudinal and minimum transverse coordinates of the index among the edge grids of that cluster, ii) when a cluster exists in the second quadrant, determine, as a nearest edge grid, a grid with maximum longitudinal and minimum transverse coordinates of the index among the edge grids of that cluster, iii) when a cluster exists in the third quadrant, determine, as a nearest edge grid, a grid with maximum longitudinal and maximum transverse coordinates of the index among the edge grids of that cluster, and iv) when a cluster exists in the fourth quadrant, determine, as a nearest edge grid, a grid with minimum longitudinal and maximum transverse coordinates of the index among the edge grids of that cluster.
- the control unit 200 may determine those nearest edge grids as grids on which continuous structures around the vehicle are located.
- FIG. 24 illustrates a result of cluster and nearest edge grid extraction when other vehicles parked continuously in a diagonal pattern exist around the vehicle.
- FIG. 25 illustrates a result of cluster and nearest edge grid extraction when other vehicles parked continuously in an orthogonal pattern exist around the vehicle.
- FIG. 26 illustrates a result of cluster and nearest edge grid extraction when a guardrail exists around the vehicle. Accordingly, the control unit 200 may cause the vehicle to park or travel by avoiding the continuous structure therearound.
- the vehicle radar system may be utilized for a rear cross traffic alert (ROTA) system.
- the ROTA system operates to determine distance movement information between frames to estimate a speed of a target vehicle moving in the parking space.
- the ROTA system may affect the detection performance of the target vehicle approaching to the vehicle when many other vehicles are parked around the vehicle.
- the longitudinal and transverse speed detection performance of the target vehicle and thus the accuracy of the approach angle thereof are reduced, which in turn causes ROTA non-warning, false warning, warning delay, and so on.
- the ROTA system When the target vehicle moving in the parking space is detected, the ROTA system operates to set a tracking gate for tracking the detected target vehicle and to perform the above-mentioned object detection in the tracking gate, as illustrated in FIG. 27 .
- it is difficult to extract the detection information most closely related to the target vehicle because not only the target vehicle but also object detection information from nearby parked vehicles and ghost detection information generated due to multiple reflections are mixed in the tracking gate. Selection of the detection information that is not closely related may affect tracking performance, particularly which may often occur in a situation where the target vehicle is dynamically accelerating/decelerating rather than traveling at a constant speed.
- the present embodiment adopts a configuration that grasps a type of parking in a parking space in which the vehicle is parked and then controls the function of the rear cross traffic alert system according to the grasped type of parking.
- the control unit 200 may grasp the type of parking in the parking space by detecting a plurality of other vehicles continuously parked around the vehicle using the clustering algorithm and edge grid extraction described in “8. Continuous Structure Monitoring”. That is, the continuous structures described in “8. Continuous Structure Monitoring” correspond to a plurality of other vehicles continuously parked around the vehicle.
- control unit 200 first checks whether the number of stationary objects in the cluster created through the above-mentioned processes is equal to or greater than a threshold value, and checks whether the number of vehicles corresponding to the nearest edge grids is equal to or greater than a threshold value.
- the control unit 200 may grasp the type of parking in the parking space, based on a first ratio (IncidentRatiobRange) between the longitudinal lengths ( ⁇ Xdedge) and the transverse lengths ( ⁇ Ydedge) of the plurality of other vehicles corresponding to the continuously arranged nearest edge grids and a second ratio (IncidentRatiobSTD) between the standard deviation (std(Xedge_ID(:))) of the longitudinal positions and the standard deviation (std(Yedge_ID(:)) of the transverse positions of the plurality of other vehicles.
- the reason why only the nearest edge grid is considered is that the portion closest to the vehicle is important and is to eliminate the problem that increases the amount of computation caused when all edge grids are considered.
- control unit 200 may grasp the type of parking in the parking space by determining a parking angle corresponding to the currently determined first and second ratios using a predefined parking angle function in relation to the combination of the first and second ratios and comparing the parking angle with a preset threshold value.
- the type of parking may include orthogonal parking, diagonal parking, and parallel parking.
- the parking angle function is a function defined to determine the type of parking in the parking space in the order of orthogonal parking, diagonal parking, and parallel parking as the first and second ratios have a larger value.
- the type of parking in the parking space is determined to be orthogonal parking when the first and second ratios have a relatively low value
- the type of parking in the parking space is determined to be diagonal parking when the first and second ratios have a relatively high value
- the type of parking in the parking space is determined to be parallel parking when the first and second ratios have a very high value.
- the parking angle function is a quantitative function using the first and second ratios as factors, which may be designed in advance by a designer and defined in the control unit 200 .
- Equation 8 illustrates an example in which the type of parking is determined according to the parking angle (IncidentAnblebEdge).
- the control unit 200 may correct a form of the tracking gate of the ROTA for tracking the target vehicle moving in the parking space in response to the grasped type of parking in the parking space.
- the control unit 200 may correct the tracking gate in the form of an ellipse with a transverse major axis, an ellipse with a diagonal major axis, and an ellipse with a longitudinal major axis, accordingly.
- the tracking gate of the ROTA may be corrected in the form of an ellipse whose major axis is directed in the movement direction of that target vehicle, resulting in an improvement in the tracking performance of the target vehicle.
- FIGS. 30 to 32 illustrate a result of actual vehicle verification data in orthogonal and diagonal parking situations.
- FIG. 33 is a flowchart for explaining a method of monitoring a surrounding environment of a vehicle in accordance with an embodiment of the present disclosure. The method of monitoring a surrounding environment of a vehicle in accordance with the present embodiment will be described with reference to FIG. 33 . A detailed description of any part that overlaps with the foregoing will be omitted and the following description will be focused on the time-series configuration thereof.
- control unit 200 extracts a stationary object among objects outside the vehicle, detected by the sensor unit 100 , by using the behavior information of the vehicle, in step S 100 .
- control unit 200 maps the stationary object extracted in step S 100 to a preset grid map, adds occupancy information to each of grids constituting the grid map according to whether the stationary object is mapped to the grid map, and calculates an occupancy probability parameter from the occupancy information added to the grids within the grid map in a plurality of frames to be monitored, the occupancy probability parameter indicating the probability that the stationary object will be located at the corresponding grid, in step S 200 .
- step S 200 the control unit 200 maps the stationary object to the grid map while updating the grid map by changing the respective indexes of the grids constituting the grid map according to the behavior information of the vehicle.
- step S 200 the control unit 200 converts the location information of the stationary object into an index corresponding to the grid map, maps the stationary object to the grid map by specifying a target grid of the grid map, corresponding to the index, adds occupancy information with a first value to the target grid to which the stationary object is mapped, and adds occupancy information with a second value to the other grids, the second value being smaller than the first value.
- step S 200 the control unit 200 calculates an occupancy probability parameter by deciding an expanded mapping area expanded by a preset range on the basis of the target grid to which the stationary object is mapped, and adding the occupancy information with the first value to each of grids constituting the expanded mapping area.
- step S 200 the control unit 200 corrects the occupancy probability parameters of grids constituting a second expanded mapping area by comparing a first expanded mapping area in the (K ⁇ 1) th frame to the second expanded mapping area in the K th frame, when the grid map is updated as the (K ⁇ 1) th frame is switched to the K th frame. Specifically, the control unit 200 specifies a first area composed of grids whose occupancy probability parameters have increased, among the grids of the second expanded mapping area, and a second area composed of grids whose occupancy probability parameters have decreased, among the grids of the first expanded mapping area, on the basis of the K th frame over the (K ⁇ 1) th frame. Then, the control unit 200 corrects the respective occupancy probability parameters of the grids constituting the second expanded mapping area in the K th frame by substituting the occupancy probability parameters of the second area with the occupancy probability parameters of the first area.
- step S 200 the control unit 200 corrects a shaded grid corresponding to a shaded area where the sensor unit 100 cannot detect an outside object in the K th frame, by using a first method of receiving an occupancy probability parameter in the (K ⁇ 1) th frame or a second method of receiving an occupancy probability parameter of a grid around the shaded grid.
- the control unit 200 corrects the shaded grid according to the first method when the speed of the vehicle is equal to or higher than a preset reference value, and corrects the shaded grid according to the second method when the speed of the vehicle is lower than the reference value.
- the control unit 200 monitors the surrounding environment of the vehicle on the basis of the occupancy probability parameter calculated in step S 200 , in step S 300 . Specifically, the control unit 200 decides a peak grid having the highest occupancy probability parameter among the grids within the expanded mapping area decided for a plurality of frames to be monitored. When the occupancy probability parameter of the peak grid is equal to or larger than a threshold value defined for the peak grid, the control unit 200 determines that the stationary object is located at the peak grid. In step S 300 , a plurality of peak grids may be determined.
- control unit 200 applies a predefined clustering algorithm to the grid specified in step S 300 to create one or more clusters composed of a plurality of grids having the same characteristic (S 400 ).
- control unit 200 uses a density based spatial clustering of applications with noise (DBSCAN) algorithm as the clustering algorithm to create one or more clusters.
- DBSCAN density based spatial clustering of applications with noise
- the clustering criterion of the DBSCAN algorithm corresponds to the distance between the peak grids where stationary objects are located.
- the control unit 200 extracts the edge grid of each cluster created in step S 400 to grasp a type of parking in a parking space by detecting a plurality of other vehicles continuously parked around the vehicle (S 500 ).
- the control unit 200 extracts grids in which a peak grid does not exist in an immediately adjacent grid, as edge grids of the cluster, from among the grids constituting the cluster, determines nearest edge grids, which are at positions close to the vehicle, among the extracted edge grids, and when the determined nearest edge grids are continuously arranged, determines those nearest edge grids as grids on which a plurality of other vehicles continuously parked around the vehicle are located.
- step S 500 the control unit 200 grasps the type of parking in the parking space, based on a first ratio between the longitudinal lengths and the transverse lengths of the plurality of other vehicles corresponding to the continuously arranged nearest edge grids and a second ratio between the standard deviation of the longitudinal positions and the standard deviation of the transverse positions of the plurality of other vehicles.
- the control unit 200 grasps the type of parking in the parking space by determining a parking angle corresponding to the currently determined first and second ratios using a predefined parking angle function in relation to the combination of the first and second ratios and comparing the parking angle with a preset threshold value.
- step S 600 the control unit controls the function of the rear cross traffic alert system in response to the type of parking grasped in step S 500 (S 600 ).
- step S 600 the control unit corrects a form of the tracking gate of the ROTA for tracking the target vehicle moving in the parking space in response to the type of parking in the parking space.
- the control unit corrects the tracking gate in the form of an ellipse with a transverse major axis, an ellipse with a diagonal major axis, and an ellipse with a longitudinal major axis, accordingly.
- the apparatus and method for monitoring the surrounding environment of a vehicle in accordance with the present embodiment may map a stationary object detected through the radar to the preset grid map, add occupancy information to each of the grids constituting the grid map depending on whether the stationary object is mapped to the grid map, and then calculate the occupancy probability parameter from the occupancy information added to each of the grids within the grid map in a plurality of frames to be monitored, the occupancy probability parameter indicating that the probability that the stationary object will be located at the corresponding grid, in order to monitor the surrounding environment of the vehicle.
- the apparatus and method can improve the detection accuracy for the outside object when monitoring the surrounding environment of the vehicle through the radar.
- the apparatus and method for monitoring the surrounding environment of a vehicle in accordance with the present embodiment may improve detection accuracy for an outside object when monitoring the surrounding environment of the vehicle through a radar.
- the apparatus and method for monitoring the surrounding environment of a vehicle in accordance with the present embodiment may map a stationary object detected through the radar to the preset grid map, add occupancy information to each of the grids constituting the grid map depending on whether the stationary object is mapped to the grid map, and then calculate the occupancy probability parameter from the occupancy information added to each of the grids within the grid map in a plurality of frames to be monitored, the occupancy probability parameter indicating that the probability that the stationary object will be located at the corresponding grid, in order to monitor the surrounding environment of the vehicle.
- the apparatus and method can improve the detection accuracy for the outside object when monitoring the surrounding environment of the vehicle through the radar.
- the controller 200 and other apparatuses, devices, units, modules, and components described herein are implemented by hardware components.
- hardware components that may be used to perform the operations described in this application where appropriate include controllers, sensors, generators, drivers, memories, comparators, arithmetic logic units, adders, subtractors, multipliers, dividers, integrators, and any other electronic components configured to perform the operations described in this application.
- one or more of the hardware components that perform the operations described in this application are implemented by computing hardware, for example, by one or more processors or computers.
- a processor or computer may be implemented by one or more processing elements, such as an array of logic gates, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a programmable logic controller, a field-programmable gate array, a programmable logic array, a microprocessor, or any other device or combination of devices that is configured to respond to and execute instructions in a defined manner to achieve a desired result.
- a processor or computer includes, or is connected to, one or more memories storing instructions or software that are executed by the processor or computer.
- Hardware components implemented by a processor or computer may execute instructions or software, such as an operating system (OS) and one or more software applications that run on the OS, to perform the operations described in this application.
- OS operating system
- the hardware components may also access, manipulate, process, create, and store data in response to execution of the instructions or software.
- processor or “computer” may be used in the description of the examples described in this application, but in other examples multiple processors or computers may be used, or a processor or computer may include multiple processing elements, or multiple types of processing elements, or both.
- a single hardware component or two or more hardware components may be implemented by a single processor, or two or more processors, or a processor and a controller.
- One or more hardware components may be implemented by one or more processors, or a processor and a controller, and one or more other hardware components may be implemented by one or more other processors, or another processor and another controller.
- processors may implement a single hardware component, or two or more hardware components.
- a hardware component may have any one or more of different processing configurations, examples of which include a single processor, independent processors, parallel processors, single-instruction single-data (SISD) multiprocessing, single-instruction multiple-data (SIMD) multiprocessing, multiple-instruction single-data (MISD) multiprocessing, multiple-instruction multiple-data (MIMD) multiprocessing, a controller and an arithmetic logic unit (ALU), a DSP, a microcomputer, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic unit (PLU), a central processing unit (CPU), a graphics processing unit (GPU), a neural processing unit (NPU), or any other device capable of responding to and executing instructions in a defined manner.
- the processor also includes a communication device, such as a computer, cellular phone, P
- the methods that perform the operations described in this application are performed by computing hardware, for example, by one or more processors or computers, implemented as described above executing instructions or software to perform the operations described in this application that are performed by the methods.
- a single operation or two or more operations may be performed by a single processor, or two or more processors, or a processor and a controller.
- One or more operations may be performed by one or more processors, or a processor and a controller, and one or more other operations may be performed by one or more other processors, or another processor and another controller.
- One or more processors, or a processor and a controller may perform a single operation, or two or more operations.
- the Instructions or software to control a processor or computer to implement the hardware components and perform the methods as described above are written as computer programs, code segments, instructions or any combination thereof, for individually or collectively instructing or configuring the processor or computer to operate as a machine or special-purpose computer to perform the operations performed by the hardware components and the methods as described above.
- the instructions or software include machine code that is directly executed by the processor or computer, such as machine code produced by a compiler.
- the instructions or software includes at least one of an applet, a dynamic link library (DLL), middleware, firmware, a device driver, an application program storing the method described herein.
- the instructions or software include higher-level code that is executed by the processor or computer using an interpreter. Programmers of ordinary skill in the art can readily write the instructions or software based on the block diagrams and the flow charts illustrated in the drawings and the corresponding descriptions in the specification, which disclose algorithms for performing the operations performed by the hardware components and the methods as described above.
- non-transitory computer-readable storage medium examples include read-only memory (ROM), random-access programmable read only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), magnetic RAM (MRAM), spin-transfer torque (STT)-MRAM, static random-access memory (SRAM), thyristor RAM (T-RAM), zero capacitor RAM (Z-RAM), twin transistor RAM (TTRAM), conductive bridging RAM (CBRAM), ferroelectric RAM (FeRAM), phase change RAM (PRAM), resistive RAM (RRAM), nanotube RRAM, polymer RAM (PoRAM), nano floating gate Memory (NFGM), holographic memory, molecular electronic memory device), insulator resistance change memory, dynamic random access memory (DRAM),
- the instructions or software and any associated data, data files, and data structures are distributed over network-coupled computer systems so that the instructions and software and any associated data, data files, and data structures are stored, accessed, and executed in a distributed fashion by the one or more processors or computers.
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Automation & Control Theory (AREA)
- Mechanical Engineering (AREA)
- Transportation (AREA)
- Mathematical Physics (AREA)
- Electromagnetism (AREA)
- Human Computer Interaction (AREA)
- Traffic Control Systems (AREA)
Abstract
Description
−Δθ_acc=Δθ_acc+Δθ−Δγ=Vs*dt=|Δγ|·cos(Δθ)â x+|Δγ·|sin(Δθ)â y−Δγ_acc=Δγ_acc+Δγ [Equation 1]
Δx k=−Δγ·cos(Δθ)
Δy k=Δγ·sin(Δθ)
Δx k_acc=Δx k_acc+Δx k
Δy k_acc=Δy k_acc+Δy k
if(|Δx k
{circle around (a)}DETERMINATION TO BE ORTHOGONAL PARKING: 0≤|IncidentAnglebEdge|≥THRESHOLD VALUE(about 22.5°)
{circle around (b)}DETERMINATION TO BE DIAGONAL PARKING: THRESHOLD VALUE(about 22.5°)≥|IncidentAnglebEdge|≤THRESHOLD VALUE(about 77.5°)
{circle around (c)}DETERMINATION TO BE PARALLEL PARKING: THRESHOLD VALUE(about 77.5°)≥|IncidentAnglebEdge|≤THRESHOLD VALUE(about 90.5°) [Equation 8]
Claims (16)
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
KR10-2021-0095582 | 2021-07-21 | ||
KR1020210095582A KR20230014344A (en) | 2021-07-21 | 2021-07-21 | Apparatus amd method for monitoring surrounding environment of vehicle |
Publications (2)
Publication Number | Publication Date |
---|---|
US20230022335A1 US20230022335A1 (en) | 2023-01-26 |
US11852716B2 true US11852716B2 (en) | 2023-12-26 |
Family
ID=84976335
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US17/870,363 Active US11852716B2 (en) | 2021-07-21 | 2022-07-21 | Apparatus and method for monitoring surrounding environment of vehicle |
Country Status (2)
Country | Link |
---|---|
US (1) | US11852716B2 (en) |
KR (1) | KR20230014344A (en) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP7192600B2 (en) * | 2019-03-20 | 2022-12-20 | 株式会社デンソー | alarm device |
WO2024178603A1 (en) * | 2023-02-28 | 2024-09-06 | 华为技术有限公司 | Occupancy grid map generation method and apparatus |
Citations (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100057361A1 (en) * | 2008-08-29 | 2010-03-04 | Toyota Motor Engineering & Manufacturing Na | System and method for stochastically predicting the future states of a vehicle |
US20140236414A1 (en) * | 2013-02-21 | 2014-08-21 | Google Inc. | Method to Detect Nearby Aggressive Drivers and Adjust Driving Modes |
US20140278052A1 (en) * | 2013-03-15 | 2014-09-18 | Caliper Corporation | Lane-level vehicle navigation for vehicle routing and traffic management |
US10286913B2 (en) * | 2016-06-23 | 2019-05-14 | Honda Motor Co., Ltd. | System and method for merge assist using vehicular communication |
US20190346854A1 (en) * | 2018-05-10 | 2019-11-14 | GM Global Technology Operations LLC | Generalized 3d inverse sensor model |
US20200019165A1 (en) * | 2018-07-13 | 2020-01-16 | Kache.AI | System and method for determining a vehicles autonomous driving mode from a plurality of autonomous modes |
US20200103523A1 (en) * | 2018-09-28 | 2020-04-02 | Zoox, Inc. | Radar Spatial Estimation |
US20200365029A1 (en) * | 2019-05-17 | 2020-11-19 | Ford Global Technologies, Llc | Confidence map building using shared data |
US20210031795A1 (en) * | 2018-01-23 | 2021-02-04 | Valeo Schalter Und Sensoren Gmbh | Correcting a position of a vehicle with slam |
US20210183241A1 (en) * | 2018-11-09 | 2021-06-17 | Sk Telecom Co., Ltd. | Apparatus and method for estimating location of vehicle |
KR20210077367A (en) | 2019-12-17 | 2021-06-25 | 현대자동차주식회사 | Vehicle body assembly structure |
US20210208242A1 (en) * | 2020-01-07 | 2021-07-08 | Metawave Corporation | Optimized proximity clustering in a vehicle radar for object identification |
US20210231769A1 (en) * | 2018-09-04 | 2021-07-29 | Robert Bosch Gmbh | Method for generating a map of the surroundings of a vehicle |
US20210241026A1 (en) * | 2020-02-04 | 2021-08-05 | Nio Usa, Inc. | Single frame 4d detection using deep fusion of camera image, imaging radar and lidar point cloud |
US20210286068A1 (en) * | 2020-03-16 | 2021-09-16 | Nio Usa, Inc. | Simulated lidar devices and systems |
US20210354708A1 (en) * | 2020-05-15 | 2021-11-18 | Zenuity Ab | Online perception performance evaluation for autonomous and semi-autonomous vehicles |
US20220126837A1 (en) * | 2020-10-27 | 2022-04-28 | Arm Limited | Vehicle-assist system |
-
2021
- 2021-07-21 KR KR1020210095582A patent/KR20230014344A/en unknown
-
2022
- 2022-07-21 US US17/870,363 patent/US11852716B2/en active Active
Patent Citations (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100057361A1 (en) * | 2008-08-29 | 2010-03-04 | Toyota Motor Engineering & Manufacturing Na | System and method for stochastically predicting the future states of a vehicle |
US20140236414A1 (en) * | 2013-02-21 | 2014-08-21 | Google Inc. | Method to Detect Nearby Aggressive Drivers and Adjust Driving Modes |
US20140278052A1 (en) * | 2013-03-15 | 2014-09-18 | Caliper Corporation | Lane-level vehicle navigation for vehicle routing and traffic management |
US10286913B2 (en) * | 2016-06-23 | 2019-05-14 | Honda Motor Co., Ltd. | System and method for merge assist using vehicular communication |
US20210031795A1 (en) * | 2018-01-23 | 2021-02-04 | Valeo Schalter Und Sensoren Gmbh | Correcting a position of a vehicle with slam |
US20190346854A1 (en) * | 2018-05-10 | 2019-11-14 | GM Global Technology Operations LLC | Generalized 3d inverse sensor model |
US20200019165A1 (en) * | 2018-07-13 | 2020-01-16 | Kache.AI | System and method for determining a vehicles autonomous driving mode from a plurality of autonomous modes |
US20210231769A1 (en) * | 2018-09-04 | 2021-07-29 | Robert Bosch Gmbh | Method for generating a map of the surroundings of a vehicle |
US20200103523A1 (en) * | 2018-09-28 | 2020-04-02 | Zoox, Inc. | Radar Spatial Estimation |
US20210183241A1 (en) * | 2018-11-09 | 2021-06-17 | Sk Telecom Co., Ltd. | Apparatus and method for estimating location of vehicle |
US20200365029A1 (en) * | 2019-05-17 | 2020-11-19 | Ford Global Technologies, Llc | Confidence map building using shared data |
KR20210077367A (en) | 2019-12-17 | 2021-06-25 | 현대자동차주식회사 | Vehicle body assembly structure |
US20210208242A1 (en) * | 2020-01-07 | 2021-07-08 | Metawave Corporation | Optimized proximity clustering in a vehicle radar for object identification |
US20210241026A1 (en) * | 2020-02-04 | 2021-08-05 | Nio Usa, Inc. | Single frame 4d detection using deep fusion of camera image, imaging radar and lidar point cloud |
US20210286068A1 (en) * | 2020-03-16 | 2021-09-16 | Nio Usa, Inc. | Simulated lidar devices and systems |
US20210354708A1 (en) * | 2020-05-15 | 2021-11-18 | Zenuity Ab | Online perception performance evaluation for autonomous and semi-autonomous vehicles |
US20220126837A1 (en) * | 2020-10-27 | 2022-04-28 | Arm Limited | Vehicle-assist system |
Also Published As
Publication number | Publication date |
---|---|
US20230022335A1 (en) | 2023-01-26 |
KR20230014344A (en) | 2023-01-30 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11852716B2 (en) | Apparatus and method for monitoring surrounding environment of vehicle | |
US20190184990A1 (en) | Method and apparatus with vehicular longitudinal velocity control | |
CN116129376A (en) | Road edge detection method and device | |
CN110045376A (en) | It can travel area obtaining method, computer readable storage medium and terminal device | |
US11498554B2 (en) | Enhanced object detection and response | |
US11087147B2 (en) | Vehicle lane mapping | |
CN112731334A (en) | Method and device for positioning vehicle by laser | |
US11971257B2 (en) | Method and apparatus with localization | |
CN113155143A (en) | Method, device and vehicle for evaluating a map for automatic driving | |
US20230024713A1 (en) | Apparatus and method for monitoring surrounding environment of vehicle | |
US20230027766A1 (en) | Apparatus and method for monitoring surrounding environment of vehicle | |
US12090952B2 (en) | Apparatus with collision warning and vehicle including the same | |
EP4102464A1 (en) | Method and apparatus with calibration | |
US12055406B2 (en) | Navigation apparatus and operation method of navigation apparatus | |
US20220260710A1 (en) | Method and system for detecting velocity of target | |
US11335012B2 (en) | Object tracking method and apparatus | |
CN114817765A (en) | Map-based target course disambiguation | |
US20220390593A1 (en) | Apparatus and method for monitoring surrounding environment of vehicle | |
US20230027728A1 (en) | Method and apparatus for determining slope of road using side view camera of vehicle | |
EP4102408A1 (en) | Method and apparatus for training neural network models to increase performance of the neural network models | |
US20240142241A1 (en) | Method and device with lane detection | |
US11835623B2 (en) | Device and method for controlling vehicle and radar system for vehicle | |
US20230154206A1 (en) | Method and apparatus with lane genertion | |
US20230290159A1 (en) | Method and apparatus for detecting land using lidar | |
CN116923405A (en) | Method and system for judging curve trafficability of automatic driving vehicle and electronic equipment |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: HYUNDAI MOBIS CO., LTD., KOREA, REPUBLIC OF Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:KANG, EUN SEOK;REEL/FRAME:060582/0505 Effective date: 20220721 |
|
FEPP | Fee payment procedure |
Free format text: ENTITY STATUS SET TO UNDISCOUNTED (ORIGINAL EVENT CODE: BIG.); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: NOTICE OF ALLOWANCE MAILED -- APPLICATION RECEIVED IN OFFICE OF PUBLICATIONS |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: NOTICE OF ALLOWANCE MAILED -- APPLICATION RECEIVED IN OFFICE OF PUBLICATIONS |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: PUBLICATIONS -- ISSUE FEE PAYMENT RECEIVED |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: PUBLICATIONS -- ISSUE FEE PAYMENT VERIFIED |
|
STCF | Information on status: patent grant |
Free format text: PATENTED CASE |